A novel role for alkyladenine DNA glycosylase in regulating alkylation-induced ER stress

Figure S1

Note: This analysis requires input data generated by an earlier script.

Setup

• Clear memory and console
  
• Load packages
  
• Get current script name

# Clear memory
rm(list=setdiff(ls(all=TRUE), c(".Random.seed")))
# Clear console
cat("\014")

Set up

this.script <- rstudioapi::getActiveDocumentContext()$path %>% basename
cat("Script:", this.script)

Script: 06_Figure_S1.Rmd

gg.get.breaks_by1 <- function(limits) {
  a <- floor(limits[1])
  b <- ceiling(limits[2])
  seq(a, b, by = 1)
}

gg.get.breaks_by2 <- function(limits) {
  a <- floor(limits[1])
  if (a %% 2 == 1) a <- a - 1
  b <- ceiling(limits[2])
  if (b %% 2 == 1) b <- b + 1
  seq(a, b, by = 2)
}

get.gg <- function(mtrx, colum.names, axis.labs, titre,
                    marker.col.false, marker.col.true="#B20000", marker.alpha=1, ttable) {

  ttable2 <- dplyr::select(ttable, PROBEID, SYMBOL, adj.P.Val) %>% 
    dplyr::group_by(PROBEID) %>%
    dplyr::slice(which.min(adj.P.Val)) %>% 
    dplyr::ungroup(.)

  df <- mtrx[,colum.names] %>% 
    magrittr::set_colnames(axis.labs) %>% 
    tibble::rownames_to_column("PROBEID")
  
  df2 <- merge(x=ttable2, y=df, by="PROBEID", all.x=FALSE, all.y=TRUE) %>% 
    dplyr::mutate(p.ok = adj.P.Val <= 0.05) %>% 
    dplyr::mutate(p.ok = as.character(p.ok)) %>% 
    dplyr::mutate(p.ok = factor(p.ok, levels=c("FALSE", "TRUE"))) %>% 
    dplyr::mutate(ID = paste0(SYMBOL, " (", PROBEID, ")")) %>% 
    dplyr::select(-PROBEID, -SYMBOL, -adj.P.Val) %>% 
    dplyr::select(ID, everything())

  res <- df2 %>% 
    ggplot(aes_string(x=axis.labs[1], y=axis.labs[2])) +
    geom_point(aes(text=ID, color=p.ok), alpha=marker.alpha) + # color=marker.col.false,
    # scale_color_discrete(name="p ≤ 0.05") +
    scale_color_manual(values=c(marker.col.false, marker.col.true), name="p ≤ 0.05") +
    scale_x_continuous(limits =c(min(mtrx), max(mtrx))) +
    scale_y_continuous(limits =c(min(mtrx), max(mtrx))) +
    geom_abline(intercept = 0, slope = 1, color="grey25") +
    geom_abline(intercept = -log2(1.75), slope = 1, color="grey25", linetype="dashed") +
    geom_abline(intercept = log2(1.75), slope = 1, color="grey25", linetype="dashed") +
    labs(title = titre)
  res
}

Load data

Load mean expression data

Table includes probe IDs, gene symbols and gene names.
Probe IDs and symbols are not unique (due to gene symbol mapping).

mean.expr.file <- "./data/01_Raw_Data_Processing.Rmd.expr_mean.txt"
matrix.df.mean <- read.table(mean.expr.file, sep="\t", header=T,
                             stringsAsFactors = FALSE) %>% 
  dplyr::select(-SYMBOL, -GENENAME) %>% 
  dplyr::distinct(PROBEID, .keep_all=TRUE) %>% 
  tibble::column_to_rownames("PROBEID")
cat("File read:", mean.expr.file)

File read: ./data/01_Raw_Data_Processing.Rmd.expr_mean.txt

rm(mean.expr.file)

Load differentially expressed probes (effect of MMS)

# list.files("data")
diff.probes.file <- "./data/02_Suppl_Table_01.Rmd.DEprobes.RDS"
stopifnot(file.exists(diff.probes.file))
test.results.probes <- readRDS(diff.probes.file)
cat("File read:", diff.probes.file)

File read: ./data/02_Suppl_Table_01.Rmd.DEprobes.RDS

rm(diff.probes.file)

x <- paste(utils::capture.output(str(test.results.probes)), collapse="<br>\n", sep="")
details::details(x, summary="Show summary of DE probes", lang=NULL)

Show summary of DE probes

List of 4
$ ko.ctrl.vs.wt.ctrl:List of 3
..$ down : chr [1:5] “1419620_at” “1424105_a_at” “1425771_at” “1438390_s_at” …
..$ up : chr [1:4] “1416958_at” “1426230_at” “1426464_at” “1438211_s_at”
..$ total: chr [1:9] “1416958_at” “1419620_at” “1424105_a_at” “1425771_at” …
$ ko.mms.vs.wt.mms :List of 3
..$ down : chr [1:3] “1419620_at” “1424105_a_at” “1438390_s_at”
..$ up : chr “1455530_at”
..$ total: chr [1:4] “1419620_at” “1424105_a_at” “1438390_s_at” “1455530_at”
$ wt.mms.vs.wt.ctrl :List of 3
..$ down : chr [1:584] “1415743_at” “1415802_at” “1415810_at” “1415822_at” …
..$ up : chr [1:232] “1416029_at” “1416108_a_at” “1416235_at” “1416432_at” …
..$ total: chr [1:816] “1415743_at” “1415802_at” “1415810_at” “1415822_at” …
$ ko.mms.vs.ko.ctrl :List of 3
..$ down : chr [1:105] “1415972_at” “1416158_at” “1417020_at” “1417073_a_at” …
..$ up : chr [1:115] “1415817_s_at” “1416100_at” “1416172_at” “1416347_at” …
..$ total: chr [1:220] “1415817_s_at” “1415972_at” “1416100_at” “1416158_at” …

rm(x)

Load toptables

# list.files()
tt.file <- "Supplemental_Data_1.xlsx"
stopifnot(file.exists(tt.file))
tt.sheet.names <- openxlsx::getSheetNames(tt.file)
# tt.sheet.names
# "ko.ctrl.vs.wt.ctrl" "ko.mms.vs.wt.mms"   "wt.mms.vs.wt.ctrl"  "ko.mms.vs.ko.ctrl"
get.xlsx.data <- function(fil, sheet) {
  openxlsx::read.xlsx(xlsxFile = fil, sheet=sheet)
}
tt.list <- lapply(tt.sheet.names, function(the.sheet) {
  res <- get.xlsx.data(fil=tt.file, sheet=the.sheet) %>% 
    dplyr::select(PROBEID, SYMBOL, logFC, adj.P.Val)
  res
}) %>% 
  set_names(tt.sheet.names)
cat("File read:", tt.file)

File read: Supplemental_Data_1.xlsx

rm(tt.file, tt.sheet.names)

y <- paste(utils::capture.output(str(tt.list)), collapse="<br>\n", sep="")
details::details(y, summary="Show summary of toptables list", lang=NULL)

Show summary of toptables list

List of 4
$ ko.ctrl.vs.wt.ctrl:‘data.frame’: 24491 obs. of 4 variables:
..$ PROBEID : chr [1:24491] “1439200_x_at” “1426464_at” “1438211_s_at” “1455265_a_at” …
..$ SYMBOL : chr [1:24491] “Rhox4b” “Nr1d1” “Dbp” “Rgs16” …
..$ logFC : num [1:24491] 4.19 3.6 3.36 3.33 3.26 …
..$ adj.P.Val: num [1:24491] 0.35717 0.00159 0.04513 0.41762 0.50034 …
$ ko.mms.vs.wt.mms :‘data.frame’: 24491 obs. of 4 variables:
..$ PROBEID : chr [1:24491] “1439200_x_at” “1415905_at” “1434137_x_at” “1448964_at” …
..$ SYMBOL : chr [1:24491] “Rhox4b” “Reg1” “Zg16” “S100g” …
..$ logFC : num [1:24491] 4 3.48 3.42 2.97 2.47 …
..$ adj.P.Val: num [1:24491] 0.0941 0.1387 0.2036 0.1737 0.3266 …
$ wt.mms.vs.wt.ctrl :‘data.frame’: 24491 obs. of 4 variables:
..$ PROBEID : chr [1:24491] “1438211_s_at” “1428942_at” “1455265_a_at” “1449233_at” …
..$ SYMBOL : chr [1:24491] “Dbp” “Mt2” “Rgs16” “Bhlha15” …
..$ logFC : num [1:24491] 4.33 4.23 4.2 4.17 4 …
..$ adj.P.Val: num [1:24491] 0.00214 0.00594 0.01936 0.02318 0.00214 …
$ ko.mms.vs.ko.ctrl :‘data.frame’: 24491 obs. of 4 variables:
..$ PROBEID : chr [1:24491] “1415905_at” “1448964_at” “1418287_a_at” “1434137_x_at” …
..$ SYMBOL : chr [1:24491] “Reg1” “S100g” “Dmbt1” “Zg16” …
..$ logFC : num [1:24491] 4.15 3.91 3.48 3.46 3.39 …
..$ adj.P.Val: num [1:24491] 0.0582 0.0638 0.0832 0.129 0.1372 …

rm(y)

Plot probes suppressed OR induced by MMS in wild-type OR knockout

Select probes to plot

wt.ko.down.up <- sort(unique(unlist(test.results.probes)))
stopifnot(!any(is.na(wt.ko.down.up)))
cat("Probes selected:", length(wt.ko.down.up))

Probes selected: 980

# matrix.df.mean[1:4,1:4]
mx.wt.ko.down.up <- matrix.df.mean[wt.ko.down.up,]
dim(mx.wt.ko.down.up)

gg.6.4 <- get.gg(mtrx=mx.wt.ko.down.up,
       colum.names = c("Wt_Ctrl", "Ko_Ctrl"),
       axis.labs = c("WT", "KO"),
       titre = "Control: Knockout versus Wild-type",
       marker.col.false = "#005AB5", # bp[3]
       marker.col.true = "#DC3220",
       marker.alpha=0.5,
       ttable=tt.list$ko.ctrl.vs.wt.ctrl)
gg.6.4b <- gg.6.4 + 
  theme_classic() +
  coord_equal()
ggplotly(gg.6.4b)

out.file.pdf <- "Figure_S1.pdf"
ggsave(filename=out.file.pdf, plot = gg.6.4b, width = 10, height=10, units = "cm")
cat("Saved:", out.file.pdf)

Saved: Figure_S1.pdf

Session info

Show/hide session info

cat("Date:", format(Sys.time(), "%a %d-%b-%Y %H:%M:%S"), "<br>\n")

Date: Sun 28-Mar-2021 22:49:25

devtools::session_info()

─ Session info ────────────────────────────────────────────────────────────────────────────────────────────────────

─ Packages ────────────────────────────────────────────────────────────────────────────────────────────────────────
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 AnnotationDbi  * 1.48.0  2019-10-29 [1] Bioconductor  
 assertthat       0.2.1   2019-03-21 [1] CRAN (R 3.6.1)
 Biobase        * 2.46.0  2019-10-29 [1] Bioconductor  
 BiocGenerics   * 0.32.0  2019-10-29 [1] Bioconductor  
 BiocManager      1.30.12 2021-03-28 [1] CRAN (R 3.6.1)
 bit              4.0.4   2020-08-04 [1] CRAN (R 3.6.1)
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 cli              2.3.1   2021-02-23 [1] CRAN (R 3.6.1)
 clipr            0.7.1   2020-10-08 [1] CRAN (R 3.6.1)
 codetools        0.2-16  2018-12-24 [2] CRAN (R 3.6.1)
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 devtools         2.3.2   2020-09-18 [1] CRAN (R 3.6.1)
 digest           0.6.27  2020-10-24 [1] CRAN (R 3.6.1)
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 ellipsis         0.3.1   2020-05-15 [1] CRAN (R 3.6.1)
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 generics         0.1.0   2020-10-31 [1] CRAN (R 3.6.1)
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 htmlwidgets      1.5.3   2020-12-10 [1] CRAN (R 3.6.1)
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 ps               1.6.0   2021-02-28 [1] CRAN (R 3.6.1)
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 R6               2.5.0   2020-10-28 [1] CRAN (R 3.6.1)
 RColorBrewer     1.1-2   2014-12-07 [1] CRAN (R 3.6.1)
 Rcpp             1.0.6   2021-01-15 [1] CRAN (R 3.6.1)
 readxl           1.3.1   2019-03-13 [1] CRAN (R 3.6.1)
 remotes          2.2.0   2020-07-21 [1] CRAN (R 3.6.1)
 rlang            0.4.10  2020-12-30 [1] CRAN (R 3.6.1)
 rmarkdown        2.7     2021-02-19 [1] CRAN (R 3.6.1)
 rprojroot        2.0.2   2020-11-15 [1] CRAN (R 3.6.1)
 RSQLite          2.2.5   2021-03-27 [1] CRAN (R 3.6.1)
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 S4Vectors      * 0.24.4  2020-04-09 [1] Bioconductor  
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 scales         * 1.1.1   2020-05-11 [1] CRAN (R 3.6.1)
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 stringi          1.5.3   2020-09-09 [1] CRAN (R 3.6.1)
 stringr          1.4.0   2019-02-10 [1] CRAN (R 3.6.1)
 superheat      * 0.1.0   2017-02-04 [1] CRAN (R 3.6.1)
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 tibble           3.1.0   2021-02-25 [1] CRAN (R 3.6.1)
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 tidyselect       1.1.0   2020-05-11 [1] CRAN (R 3.6.1)
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 viridisLite      0.3.0   2018-02-01 [1] CRAN (R 3.6.1)
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 xfun             0.22    2021-03-11 [1] CRAN (R 3.6.1)
 xml2             1.3.2   2020-04-23 [1] CRAN (R 3.6.1)
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 zlibbioc         1.32.0  2019-10-29 [1] Bioconductor  

[1] /homedirs26/sghms/bms/users/anohturf/R/x86_64-pc-linux-gnu-library/3.6
[2] /opt/R/3.6.1/lib64/R/library